Octopus v4: Graph of language models

Use our open-sourced GitHub (https://github.com/NexaAI/octopus-v4) to try Octopus v4 mod- els (https://huggingface.co/NexaAIDev/Octopus-v4),

Graph data structures have emerged as a powerful tool for representing complex relationships and dependencies in various domains.

Graphs offer several advantages over other data structures, including efficient traversal, pattern discovery, and the ability to model real-world networks. Many prominent companies have leveraged graph-based approaches to enhance their products and services.

Recent advancements in graph neural networks (GNNs) [51, 45, 35, 47] have pushed the boundaries of graph-based learning, enabling the processing of graph-structured data for tasks such as node classification, link prediction, and graph generation. Frontier research in this area includes the development of more expressive and efficient GNN architectures, such as Graph Attention Networks (GATs) [42, 8] and Graph Convolutional Networks (GCNs) [50, 43], which have achieved state-of-the-art performance on various graph-related tasks.